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Scientific Computing with Python 3

You're reading from  Scientific Computing with Python 3

Product type Book
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Pages 332 pages
Edition 1st Edition
Languages
Authors (3):
Claus Führer Claus Führer
Profile icon Claus Führer
Jan Erik Solem Jan Erik Solem
Olivier Verdier Olivier Verdier
View More author details

Table of Contents (23) Chapters

Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
1. Getting Started 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Accessing array entries


Array entries are accessed by indexes. In contrast to vector coefficients two indexes are needed to access matrix coefficients. These are given in one pair of brackets. This distinguishes the array syntax from a list of lists. There, two pairs of brackets are needed to access elements.

M = array([[1., 2.],[3., 4.]])
M[0, 0] # first row, first column: 1.
M[-1, 0] # last row, first column: 3.

Basic array slicing

Slices are similar to those of lists except that there might now be in more than one dimension:

  • M[i,:] is a vector filled by the row i of M.
  • M[:,j] is a vector filled by the column i of M.
  • M[2:4,:] is a slice of 2:4 on the rows only.
  • M[2:4,1:4] is a slice on rows and columns.

The result of matrix slicing is given in the following figure (Figure 4.1):

Figure 4.1: The result of matrix slicing

Note

Omitting a dimension

If you omit an index or a slice, NumPy assumes you are taking rows only. M[3] is a vector that is a view on the third row of and M[1:3] is a...

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